An Explicit Feature Selection Strategy for Predictive Models of the S&P 500 Index

Abstract

The focus of this study is the selection of an appropriate set of features for a feed forward neural network model used to predict both future market direction and future returns for the S&P 500 Index. The experimental results provide evidence that the proposed feature selection process may result in a more successful prediction model. However, the study also indicates that the problem domain may need to be limited to predicting monthly instead of daily movements. In addition, the proposed process could be more useful for predicting the future market direction rather than actual returns. 1. Introduction While the application of neural networks to financial forecasting is beginning to receive academic attention [Freedman 1995], the issue of feature selection for financial forecasting problems has been largely ignored. Feature selection refers to choosing a subset of parameters (or features) from a larger pool of input information (technical and/or fundamental indicators) for designing a..